首先，需要导入tushare库和其它必要的库，如下所示： ```python import tushare as ts import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline from sklearn.model_selection import train_test_split import warnings warnings.filterwarnings('ignore') ``` 然后，可以使用tushare获取商品期货行情数据，如下所示： ```python # 获取商品期货行情数据 df = ts.futures.get_hist_data('RB', start='2010-01-01', end='2022-01-01') df = df.sort_index() ``` 在获取数据后，需要对数据进行一些必要的处理，例如将行情数据转换为日频率，计算收益率等。具体代码如下所示： ```python # 将行情数据转换为日频率 df = df.resample('D').last().dropna() # 计算收益率 df['ret'] = df['close'].pct_change() df = df.dropna() ``` 然后，可以根据数据构建横截面多因子模型。在本例中，我们选择了以下因子： 1. 收益率因子 2. 历史波动率因子 3. 历史收益率因子 4. 历史成交量因子 具体代码如下所示： ```python # 构建横截面多因子模型 class CrossSectionalCTA: def __init__(self): self.lin_reg = Pipeline([ ('scaler', StandardScaler()), ('pca', PCA(n_components=2)), ('lr', LinearRegression()) ]) def fit(self, X, y): self.lin_reg.fit(X, y) def predict(self, X): return self.lin_reg.predict(X) def get_factors(self, df): factors = pd.DataFrame() factors['ret'] = df['ret'] factors['volatility'] = df['ret'].rolling(window=20).std() factors['past_returns'] = df['ret'].rolling(window=20).mean() factors['volume'] = df['volume'].rolling(window=20).mean() factors = factors.dropna() return factors def get_X_y(self, factors): X = factors.drop('ret', axis=1) y = factors['ret'].values return X, y ``` 最后，可以使用构建好的横截面多因子模型对商品期货进行交易，如下所示： ```python # 使用横截面多因子模型进行交易 ccta = CrossSectionalCTA() factors = ccta.get_factors(df) X, y = ccta.get_X_y(factors) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) ccta.fit(X_train, y_train) y_pred = ccta.predict(X_test) y_pred = np.sign(y_pred) y_pred[y_pred == 0] = 1 ret = y_pred * y_test cum_ret = np.cumprod(1 + ret) - 1 ``` 完整代码如下所示： ```python import tushare as ts import pandas as pd import numpy as np from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline from sklearn.model_selection import train_test_split import warnings warnings.filterwarnings('ignore') # 构建横截面多因子模型 class CrossSectionalCTA: def __init__(self): self.lin_reg = Pipeline([ ('scaler', StandardScaler()), ('pca', PCA(n_components=2)), ('lr', LinearRegression()) ]) def fit(self, X, y): self.lin_reg.fit(X, y) def predict(self, X): return self.lin_reg.predict(X) def get_factors(self, df): factors = pd.DataFrame() factors['ret'] = df['ret'] factors['volatility'] = df['ret'].rolling(window=20).std() factors['past_returns'] = df['ret'].rolling(window=20).mean() factors['volume'] = df['volume'].rolling(window=20).mean() factors = factors.dropna() return factors def get_X_y(self, factors): X = factors.drop('ret', axis=1) y = factors['ret'].values return X, y # 获取商品期货行情数据 df = ts.futures.get_hist_data('RB', start='2010-01-01', end='2022-01-01') df = df.sort_index() # 将行情数据转换为日频率 df = df.resample('D').last().dropna() # 计算收益率 df['ret'] = df['close'].pct_change() df = df.dropna() # 使用横截面多因子模型进行交易 ccta = CrossSectionalCTA() factors = ccta.get_factors(df) X, y = ccta.get_X_y(factors) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) ccta.fit(X_train, y_train) y_pred = ccta.predict(X_test) y_pred = np.sign(y_pred) y_pred[y_pred == 0] = 1 ret = y_pred * y_test cum_ret = np.cumprod(1 + ret) - 1 ```